For years, the National Oceanographic and Atmospheric Administration has provided hydrologic and snow services to predict water amounts for the continental United States.
Hydrologic services include flood warnings and drought warnings, while snow services include depth and water equivalent data.
However, the two services do not overlap, making accurate data analysis and prediction difficult.
University of Texas at Arlington researcher Yu Zhang, an assistant professor in the Civil Engineering Department, has earned a $515,565 grant from NOAA to apply inputs from the joint polar satellite system, or JPSS, to create a paradigm which could be applied to the National Water Model and allow both services to be considered in prediction and warnings for snow melt/runoff, flooding and similar events.
The National Water Model is a hydrologic model that simulates observed and forecast streamflow over the entire continental United States by simulating the water cycle with mathematical representations of the different processes and how they fit together.
This complex representation of physical processes such as snow melt/infiltration and movement of water through the soil layers varies significantly with changing elevations, soils, vegetation types and a host of other variables.